Bearings-Only Sensor Scheduling Using Circular Statistics

Abstract

In this paper, we introduce a novel approach for scheduled tracking of a moving target based on bearings-only sensors. Unlike classical approaches that are typically based on the extended or unscented Kalman filter, we rely on circular statistics to describe probability distributions for angular measurements more accurately. As the energy available to sensors is limited in many scenarios, we introduce a scheduling algorithm that selects a subset of two sensors to be active at any given time step while minimizing the uncertainty of the state estimate. This is done by anticipating possible future measurements. We evaluate the proposed method in simulations and compare it to an UKF-based solution. Our evaluation demonstrates the superiority of the presented approach, particularly when high measurement uncertainty makes consideration of the circular geometry necessary.

Toronto Intelligent Systems Lab Co-authors